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- W2470792647 abstract "This chapter discusses the representational specificity and concept learning. When a young child is fed by an adult, at first she assumes that all generally similar creatures, that is, all adults, would treat her equally well. With more experience, she comes to expect food from a smaller group of adults, such as all familiar adults, and then from a yet more specific class of creatures, such as mother, father, and grandmother (MFG). On the other hand, the child could initially expect feeding from a very specific set, mother only, and with experience generalize to female relatives (MG) and then to larger classes, such as MFG. Clearly, both approaches, starting with general classes or starting with specific classes, can produce appropriate results. Both converge toward an optimal level of generality. Even though the two approaches attain the same result, one could be more efficient. For instance, if one approach generally requires fewer examples before converging on the correct class, that method could be used in machine learning algorithms and would be expected to be used in natural systems." @default.
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- W2470792647 date "1991-01-01" @default.
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- W2470792647 title "Representational Specificity and Concept Learning" @default.
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- W2470792647 doi "https://doi.org/10.1016/b978-1-4832-0773-5.50009-2" @default.
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